flow-nexus-neural

Flow Nexus Neural Networks

Deploy, train, and manage neural networks in distributed E2B sandbox environments. Train custom models with multiple architectures (feedforward, LSTM, GAN, transformer) or use pre-built templates from the marketplace.

Prerequisites

# Add Flow Nexus MCP server
claude mcp add flow-nexus npx flow-nexus@latest mcp start

# Register and login
npx flow-nexus@latest register
npx flow-nexus@latest login

Core Capabilities

1. Single-Node Neural Training

Train neural networks with custom architectures and configurations.

Available Architectures:

• feedforward - Standard fully-connected networks
• lstm - Long Short-Term Memory for sequences
• gan - Generative Adversarial Networks
• autoencoder - Dimensionality reduction
• transformer - Attention-based models

Training Tiers:

• nano - Minimal resources (fast, limited)
• mini - Small models
• small - Standard models
• medium - Complex models
• large - Large-scale training

Example: Train Custom Classifier

mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "feedforward",
      layers: [
        { type: "dense", units: 256, activation: "relu" },
        { type: "dropout", rate: 0.3 },
        { type: "dense", units: 128, activation: "relu" },
        { type: "dropout", rate: 0.2 },
        { type: "dense", units: 64, activation: "relu" },
        { type: "dense", units: 10, activation: "softmax" }
      ]
    },
    training: {
      epochs: 100,
      batch_size: 32,
      learning_rate: 0.001,
      optimizer: "adam"
    },
    divergent: {
      enabled: true,
      pattern: "lateral", // quantum, chaotic, associative, evolutionary
      factor: 0.5
    }
  },
  tier: "small",
  user_id: "your_user_id"
})

Example: LSTM for Time Series

mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "lstm",
      layers: [
        { type: "lstm", units: 128, return_sequences: true },
        { type: "dropout", rate: 0.2 },
        { type: "lstm", units: 64 },
        { type: "dense", units: 1, activation: "linear" }
      ]
    },
    training: {
      epochs: 150,
      batch_size: 64,
      learning_rate: 0.01,
      optimizer: "adam"
    }
  },
  tier: "medium"
})